Doppler spectrum analysis is used in a wide variety of applications, such as ultrasound clinical diagnoses. Ultrasound Doppler-derived waveforms provide important diagnostic information regarding examined blood flow. Perhaps the two most commonly used waveforms are maximum frequency and mean frequency waveforms. From these derived waveforms, other Doppler measurement parameters and indices, such as the pulsatility index and the resistance index, can be further derived. However, accurate and robust measurement of the derived waveforms is a crucial task for the performance of a diagnostic ultra-sound imaging system.
In operation, an ultrasound transducer is brought into contact with a person and is positioned to propagate ultrasound energy into the body and receive reflected energy from an object of interest. For example, the energy may be directed at vessel lumen.
The ultrasound energy that is received by the transducer is used to generate a succession of frequency spectra. The received energy has a range of frequency components. Each frequency spectrum represents the intensity values for the frequency components during a particular time. The intensity values of a frequency spectrum are referred to as "frequency bins." A single frequency spectrum may be formed of 256 frequency bins.
Ultrasound Doppler-derived waveforms are then formed from the generated frequency spectra. A maximum frequency waveform defines the peak blood speed. For arterial flow, the speed of blood flow increases during the systolic phase of the heart cycle and declines during the diastolic phase. A conventional approach of deriving the maximum frequency waveform or other second-generation Doppler signals is to freeze images of the frequency spectra and manually determine relevant values. For example, the user may move a track ball to trace the outer edge of a spectrum. This is a time consuming and labor intensive process. Moreover, the accuracy of the process relies upon the skills of the user. Manual measurement may not provide accurate results over time. Besides accuracy, an important requirement is measurement robustness. In the clinical environment, the measurement must be performed consistently in normal and low signal-to-noise ratio conditions and should perform consistently for different users and different system settings
Automated methods of deriving Doppler waveforms are known. In order to generate accurate and robust Doppler-derived waveforms, there must be a separation between the Doppler spectral signal and any background noise. In one approach, the noise threshold is defined as a percentage of the average of noise and signal in a chosen processing domain. This percentage is a fixed number for all patients. Since a noise level relative to the signal level can change dramatically from patient to patient, the noise threshold floats depending upon the patient. In this approach, the noise level selection and waveform detection are subject to the signal strength. When the signal-to-noise ratio (SNR) is low, the fixed percentage will set the noise threshold below the actual noise level. The result is noisy derived waveforms. On the other hand, when the SNR is high, the fixed percentage sets the noise threshold above the actual noise level. Potentially, this results in missing weak signals in the systolic peaks. A second concern with this approach is that the average of the noise and signal can change dramatically from one frequency spectrum ("column") to the next, so that the noise threshold changes dramatically with time. While background noise does change with time, the actual noise level is relatively stable. As a result, this automated approach introduces a level of unpredictability.
U.S. Pat. No. 5,287,753 to Routh et al. describes an automated technique for continuously determining and displaying the peak and mean velocities of spectral Doppler information. To distinguish the signal from the noise, a number of spectral lines are selected from a single cardiac cycle. For each selected line, a peak intensity is determined. Then, an assumed noise threshold (e.g., 3 dB below the peak frequency) is applied. The average of the data points above the noise threshold and the average of the data points below the threshold are calculated. After the above-threshold average and the below-threshold average are determined for all of the selected lines, the separate averages are averaged for all of the lines. It is then assumed that the above-threshold average is the value of average signal and the below-threshold average is the value of average noise. The SNR is calculated by dividing the average signal value by the average noise value and by a constant (K) that is a function of the display rate of the spectral lines. The calculation of SNR is updated for each subsequent cardiac cycle.
One concern with the Routh et al. approach is that the selection of the assumed noise threshold (e.g., 3 dB below the peak intensity) is somewhat arbitrary. In a high SNR environment, a portion of the signal is likely to be included in the calculation of the average noise value. On the other hand, in a low SNR environment, part of the noise may be included in the determination of the average signal value. Noise threshold derived from the average SNR is still subject to the change in signal strength. An inaccurate noise threshold makes maximum frequency detection less robust. The derived waveform can be noisy when the threshold is low. The systolic peak can be missed when the noise threshold is improperly set high.
Another automated approach is described in U.S. Pat. No. 5,271,404 to Corl et al. The calculation of the SNR begins with determining a region of lowest spectral amplitude in a region of highest spectral amplitude for each frequency spectrum. For example, in a frequency spectrum consisting of 256 bins, the bins may be divided into bands and the value of each bin within a band may be summed with the values of the other bins within the band. The accumulated value for a band is compared to the accumulated values of other bands within the same spectrum to find the highest value band and the lowest value band. After the two bands are found for the various frequency spectra, the corresponding values for the bands are incorporated into a running weighted average value for the highest value bands and a second running weighted average value for the lowest value bands. The weighting factor is selected to determine how many of the most recent frequency spectra contribute significantly to the average values. The ratio of the two average values is then identified as the SNR. A concern is that the selection of only the highest value bands and only the lowest value bands does not provide a sufficient sampling for determining an accurate SNR.
After the SNR is selected using one of the above approaches or using a different approach, the maximum Doppler frequency waveform is derived. This may be achieved by defining a search block of contiguous bins. The location of the maximum Doppler frequency may be identified as the value of the first bin after which all of the bins in the search block have an intensity value greater than the noise threshold. For example, the search block may comprise ten bins. There are two potential problems with this design. First, the noise threshold must be sufficiently high to prevent long noise from being detected as the beginning of the Doppler signal. This high noise threshold may chop off systolic peaks. Second, there is a tradeoff within the selection of bins in the search block. The search block should be sufficiently long to avoid interpreting a small noise spike as Doppler signal, but should be sufficiently short to avoid missing narrow spectrum, such as from femoral arteries.
Yet another problem in automatic Doppler waveform measurements relates to signal crossover. The selection of a domain for maximum Doppler frequency detection is defined to a side of a baseline which has a higher signal energy. This definition operates well as long as the energy difference between the upper and lower sides of the baseline is large. However, in bi-directional flow, the reversals of flow direction can generate Doppler signals on both sides of the baseline with comparable energy. The manner of managing trace crossover is somewhat problematic. Higher energy definition can become unstable and cause the waveform trace to suddenly cross the baseline. This sudden crossing may occur more than once if energy detection is within a noisy environment. The sudden jumps produce bends within the waveform and make the appearance of the waveform less desirable.
What is needed is a method and system for processing Doppler data in an automated manner and with high degrees of accuracy and reliability within a range of different environments.